/Flight-Price-Predictor

A flight price prediction app that leverages machine learning algorithms to forecast future flight prices, assisting travelers in finding the best deals and planning their trips more effectively.

Primary LanguageJupyter NotebookMIT LicenseMIT

Flight Price Predictor Predictor

The Flight Price Predictor is a web application built with Flask that predicts flight prices based on historical data and various parameters. This project aims to provide users with an estimate of future flight costs, helping them plan and book their travels more efficiently.

Features

  • Prediction Model: Utilizes machine learning models to predict flight prices.

  • User-friendly Interface: Interactive web interface powered by Flask for ease of use.

  • Historical Data Analysis: Factors in historical flight data to enhance prediction accuracy.

  • Customizable: Allows users to input different parameters for personalized predictions.

Prerequisites

  • Python 3.8
  • Dependencies List -

    • Scikit Learn: A machine learning library in Python.

      • Install: pip install scikit-learn
      • Purpose: Utilized for implementing machine learning models and data preprocessing in the project.

    • Pandas: A powerful data manipulation and analysis library.

      • Install: pip install pandas
      • Purpose: Used for handling and processing structured data.

    • NumPy: A fundamental package for scientific computing with Python.

      • Install: pip install numpy
      • Purpose: Provides support for large, multi-dimensional arrays and matrices, along with mathematical functions to operate on these arrays.

    • Seaborn: A data visualization library based on Matplotlib.

      • Install: pip install seaborn
      • Purpose: Enhances the visual appeal of statistical graphics created with Matplotlib.

    • Matplotlib: A comprehensive library for creating static, interactive, and animated plots.

      • Install: pip install matplotlib
      • Purpose: Essential for generating various types of plots and charts.

    • Joblib: A module for serializing and deserializing Python objects.

      • Comes with Python standard library, no separate installation required.
      • Purpose: Used for saving and loading machine learning models or other Python objects.

Acknowledgements

  • Flask: Special thanks to the Flask framework for making web development in Python elegant and straightforward.

  • Scikit-learn: We appreciate the Scikit-learn library for providing powerful tools for predictive modeling.

  • NumPy: Heartfelt thanks to the NumPy community for developing a fundamental library that forms the backbone of numerical computing in Python.

  • Pandas: Special appreciation to the Pandas development team for creating an indispensable tool for data manipulation and analysis, making our project more efficient and effective.

  • Matplotlib: A big thanks to the Matplotlib developers for providing an extensive and flexible plotting library, adding a visual dimension to our data exploration and presentation.

  • Seaborn: We express our gratitude to the Seaborn community for enhancing our data visualization capabilities with a high-level interface to Matplotlib, making our plots more aesthetically pleasing and informative.

  • Open Source Community: Gratitude to the broader open-source community for sharing knowledge and fostering collaboration.

Contact

Email : miteshgupta2711@gmail.com

Linkedin : https://www.linkedin.com/in/mitesh-gupta/

Twitter : https://twitter.com/mg_mitesh